Extending LLM models using Azure services and tools
β±οΈ Length: 1.2 total hours
β 4.25/5 rating
π₯ 21,073 students
π July 2025 update
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- Course Overview
- Unlock the potential of Large Language Models (LLMs) by seamlessly integrating them with Microsoft’s comprehensive Azure cloud platform.
- This focused 1.2-hour module provides a practical, hands-on approach to extending the capabilities of pre-trained LLMs for diverse business and technical applications.
- Dive into the core concepts and best practices for leveraging Azure’s robust infrastructure and specialized AI services to enhance and customize your LLM deployments.
- Discover how to build sophisticated AI-powered solutions that go beyond the standard offerings of off-the-shelf LLMs, tailored to your specific needs.
- Designed for professionals seeking to innovate and gain a competitive edge by harnessing advanced AI capabilities through Azure.
- Target Audience
- Software Developers looking to embed advanced AI into their applications.
- Data Scientists aiming to fine-tune and operationalize LLMs.
- AI Engineers seeking to build scalable and performant LLM solutions.
- Technical Architects designing cloud-native AI strategies.
- Business Analysts and Product Managers interested in understanding the practical applications of LLM extension.
- Requirements / Prerequisites
- A foundational understanding of cloud computing concepts, particularly within the Microsoft Azure ecosystem.
- Basic familiarity with machine learning and artificial intelligence principles.
- Experience with programming languages commonly used in AI development (e.g., Python) is beneficial but not strictly required for conceptual understanding.
- A Microsoft Azure account with appropriate permissions to access and deploy resources.
- Conceptual grasp of what Large Language Models are and their general capabilities.
- Skills Covered / Tools Used
- Azure AI Services: Deep dive into services like Azure OpenAI Service for accessing and managing powerful LLMs, Azure Machine Learning for custom model development and deployment, and Azure Cognitive Services for augmented intelligence.
- LLM Integration Strategies: Learn techniques for connecting LLMs with external data sources, APIs, and other Azure services to enrich their responses and expand their functionalities.
- Prompt Engineering Best Practices: Master the art of crafting effective prompts to guide LLMs towards desired outputs, including context setting, few-shot learning, and parameter tuning within the Azure environment.
- Data Augmentation & Fine-Tuning: Explore methods for preparing custom datasets and fine-tuning pre-trained LLMs on Azure to achieve specialized performance on domain-specific tasks.
- Scalability & Deployment: Understand how to deploy and scale your LLM-enhanced applications using Azure’s robust infrastructure, ensuring reliability and performance.
- Security & Governance: Gain insights into implementing secure access controls and governance policies for your LLM deployments on Azure.
- Application Scenarios: Discover practical use cases such as advanced chatbots, content generation tools, intelligent search engines, and data analysis assistants built on Azure LLMs.
- Azure CLI & SDKs: Familiarize yourself with command-line interfaces and software development kits for programmatic interaction with Azure AI services.
- Benefits / Outcomes
- Enhanced LLM Capabilities: Move beyond generic LLM performance by tailoring models to specific industry needs and enterprise challenges.
- Accelerated Innovation: Rapidly prototype and deploy AI-driven solutions, enabling quicker time-to-market for innovative products and services.
- Cost-Effective AI Solutions: Leverage Azure’s scalable and managed services to optimize AI infrastructure costs and operational overhead.
- Competitive Advantage: Equip your organization with advanced AI capabilities that differentiate you in the market.
- Improved Operational Efficiency: Automate tasks, streamline workflows, and gain deeper insights through intelligent LLM-powered applications.
- Future-Proofing: Stay ahead of the curve by mastering the integration of cutting-edge LLM technology with a leading cloud platform.
- Hands-on Experience: Gain practical, transferable skills through guided exercises and real-world application examples.
- PROS
- Concise and Focused: Delivers essential knowledge within a short timeframe, ideal for busy professionals.
- Azure Ecosystem Integration: Emphasizes practical application within the widely adopted Azure cloud.
- High Student Engagement: Indicates strong learner satisfaction and perceived value (4.25/5 rating).
- Up-to-Date Content: Regularly updated (July 2025), ensuring relevance with rapidly evolving AI technology.
- Large Student Base: Suggests proven effectiveness and comprehensive learning experience for a broad audience.
- CONS
- Limited Depth on Foundational LLM Theory: As a course focused on extending LLMs, it may not delve extensively into the theoretical underpinnings of LLM architecture or training from scratch.
Learning Tracks: English,IT & Software,Other IT & Software
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